{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-02-14T12:35:57.313809Z",
     "start_time": "2025-02-14T12:35:57.283029Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "us_file_path = \"./youtube_video_data/US_video_data_numbers.csv\"\n",
    "uk_file_path = \"./youtube_video_data/GB_video_data_numbers.csv\"\n",
    "# t1 = np.loadtxt(us_file_path,delimiter=\",\",dtype=\"int\",unpack=True)\n",
    "t_us = np.loadtxt(us_file_path,delimiter=\",\",dtype=\"int\")\n",
    "# 取评论的数据\n",
    "t_us_comments = t_us[:,-1]\n",
    "\n",
    "# 选择比5000小的数据\n",
    "t_us_comments = t_us_comments[t_us_comments<=5000]\n",
    "\n",
    "print(t_us_comments.shape)\n",
    "# 怎么知道分多少，打印最大和最小值\n",
    "print(t_us_comments.max(),t_us_comments.min())\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1373,)\n",
      "4995 0\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-14T12:35:05.099228Z",
     "start_time": "2025-02-14T12:34:51.301859Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 剩余多少数据\n",
    "print(t_us_comments.shape)\n",
    "d = 50\n",
    "\n",
    "bin_nums = (t_us_comments.max()-t_us_comments.min())//d\n",
    "\n",
    "# 绘图\n",
    "\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "\n",
    "plt.hist(t_us_comments,bin_nums)\n",
    "\n",
    "plt.show()"
   ],
   "id": "2a245f80bb680f01",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1688,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T01:25:21.532891Z",
     "start_time": "2025-02-16T01:25:19.125521Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "us_file_path = \"./youtube_video_data/US_video_data_numbers.csv\"\n",
    "uk_file_path = \"./youtube_video_data/GB_video_data_numbers.csv\"\n",
    "# t1 = np.loadtxt(us_file_path,delimiter=\",\",dtype=\"int\",unpack=True)\n",
    "t_us = np.loadtxt(us_file_path,delimiter=\",\",dtype=\"int\")\n",
    "#取评论的数据\n",
    "t_us_comments = t_us[:,-1]\n",
    "#选择比5000小的数据\n",
    "t_us_comments = t_us_comments[t_us_comments<=5000]\n",
    "print(t_us_comments.max(),t_us_comments.min())\n",
    "d = 50\n",
    "bin_nums = (t_us_comments.max()-t_us_comments.min())//d\n",
    "#绘图\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.hist(t_us_comments,bin_nums)\n",
    "plt.show()"
   ],
   "id": "a392ad8c66fa7825",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4995 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T01:39:15.785644Z",
     "start_time": "2025-02-16T01:39:15.430916Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "us_file_path = \"./youtube_video_data/US_video_data_numbers.csv\"\n",
    "uk_file_path = \"./youtube_video_data/GB_video_data_numbers.csv\"\n",
    "# t1 = np.loadtxt(us_file_path,delimiter=\",\",dtype=\"int\",unpack=True)\n",
    "t_uk = np.loadtxt(uk_file_path,delimiter=\",\",dtype=\"int\")\n",
    "#选择喜欢数比50万小的数据\n",
    "# t_uk = t_uk[t_uk[:,1]<=500000]\n",
    "t_uk_comment = t_uk[:,-1]\n",
    "t_uk_like = t_uk[:,1]\n",
    "plt.xlabel(\"like\")\n",
    "plt.ylabel(\"comment\")\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.scatter(t_uk_like,t_uk_comment) # 画散点图，观察点的分布情况\n",
    "plt.show()"
   ],
   "id": "8fed55bd36257716",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-16T01:43:16.932574Z",
     "start_time": "2025-02-16T01:43:16.918881Z"
    }
   },
   "cell_type": "code",
   "source": [
    "t1 = np.loadtxt(us_file_path,delimiter=\",\",dtype=\"int\",unpack=True)  # unpack相当于转置\n",
    "t1.shape"
   ],
   "id": "d20712ddd78a0d68",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1688)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
